{"id":"W3118349307","doi":"10.3390/f12010076","title":"Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China","year":2021,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université de Montréal","funders":"National Key Research and Development Program of China","keywords":"Normalized Difference Vegetation Index; Vegetation (pathology); Environmental science; Remote sensing; Advanced very-high-resolution radiometer; Moderate-resolution imaging spectroradiometer; Enhanced vegetation index; Spectroradiometer; Physical geography; Climate change; Geography; Vegetation Index; Satellite; Ecology; Reflectivity","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003835798,0.0002702936,0.0002139771,0.001334882,0.0003995116,0.0003411597,0.0002879767,0.0002048285,0.0003166675],"category_scores_gemma":[0.0003467481,0.0001586146,0.000302353,0.001508078,0.0002514806,0.0002366727,0.0002782269,0.000114573,0.00004948235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008203252,"about_ca_system_score_gemma":0.0007811658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09981608,"about_ca_topic_score_gemma":0.1314572,"domain_scores_codex":[0.9998038,0.00001765548,0.00001857631,0.00006052676,0.00004633296,0.00005306384],"domain_scores_gemma":[0.9997522,0.00003447323,0.00007561767,0.00001871701,0.00006315582,0.0000558881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007426843,0.00005316454,0.9761686,0.00008750174,0.0001160144,0.0006266629,0.001162437,0.00113057,0.005829396,0.0001846628,0.0004645109,0.01410222],"study_design_scores_gemma":[0.000001498988,0.000008276931,0.998753,0.000002525874,0.00001178216,0.00004195026,0.0001973111,0.0006452822,0.00009950411,0.000008103746,0.0002270704,0.000003783273],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992668,0.0001275623,0.00005745364,0.00002390599,0.000002706798,0.000004137609,0.0002274827,0.000006482003,0.0002833796],"genre_scores_gemma":[0.9990958,0.0001025476,0.0001306142,0.000008293308,0.000004354005,0.000007568757,0.0003814202,0.00000147065,0.00026782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09981608,"threshold_uncertainty_score":0.1984702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007781610941033945,"score_gpt":0.2080833691978968,"score_spread":0.2003017582568628,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}